Enlarging the terminal region of nonlinear model predictive control using the support vector machine method

Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.automatica.2006.02.023

Title:	Enlarging the terminal region of nonlinear model predictive control using the support vector machine method
Authors:	Ong, C.J. Sui, D. Gilbert, E.G.
Keywords:	Constraints Nonlinear model predictive control Stability Support vector machine Terminal conditions
Issue Date:	Jun-2006
Citation:	Ong, C.J., Sui, D., Gilbert, E.G. (2006-06). Enlarging the terminal region of nonlinear model predictive control using the support vector machine method. Automatica 42 (6) : 1011-1016. ScholarBank@NUS Repository. https://doi.org/10.1016/j.automatica.2006.02.023
Abstract:	In this paper, receding horizon model predictive control (RHMPC) of nonlinear systems subject to input and state constraints is considered. We propose to estimate the terminal region and the terminal cost off-line using support vector machine learning. The proposed approach exploits the freedom in the choices of the terminal region and terminal cost needed for asymptotic stability. The resulting terminal regions are large and, hence provide for large domains of attraction of the RHMPC. The promise of the method is demonstrated with two examples. © 2006 Elsevier Ltd. All rights reserved.
Source Title:	Automatica
URI:	http://scholarbank.nus.edu.sg/handle/10635/60198
ISSN:	00051098
DOI:	10.1016/j.automatica.2006.02.023
Appears in Collections:	Staff Publications

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